AI Governance, Risk and Oversight

• Examine the board's role and accountability in relation to artificial intelligence, including how directors' duties of care, diligence and good faith apply when AI systems materially affect organisational decisions, risk exposure or stakeholder outcomes.
• Identify the categories of risk introduced by AI at a governance level, including strategic, ethical, operational, regulatory and reputational risks, and apply existing risk governance frameworks to assess how AI-specific characteristics should be captured, documented and escalated within the organisation's risk management processes.
• Apply governance principles of accountability, transparency and integrity to the oversight of AI, including identifying the governance structures, policy instruments and reporting mechanisms that support defensible board oversight, and document and report findings in a form suitable for the governance professional's report to the board.
• Identify the indicators of AI bias and governance accountability failure and examine the structures boards need to have in place to satisfy themselves that AI systems operating within their organisations are performing fairly, within sanctioned parameters and consistently with the organisation's stated values.

Section A – Governance Principles

• What is AI governance and why does it concern the board - governance practice, AI and the responsibilities of the governing body, why AI creates distinct governance challenges.
• Core principles and their application to AI - the application and extension of established governance principles and frameworks to AI, the limitations of this, embedding AI across the three interconnected dimensions of frameworks, structures, and tools.
• AI governance and oversight – accountability, transparency and explainability, integrity and ethical conduct, stewardship and long term value, application across organisational contexts.
• The Australian regulatory landscape – existing legal frameworks, evolving political frameworks, emerging or anticipated reform.
• Categories of AI risk for the governance professional - a structured view of the primary AI risk categories and their governance indicators.
• Agentic AI and board accountability – guidance for governance professionals for agentic AI which raises accountability questions that existing governance frameworks are ill-equipped to answer.
• AI bias and governance accountability – how AI bias arises, common sources of AI bias, the board’s responsibility, appropriate bias testing and monitoring processes, human review mechanisms, clear response plans for bias-related incidents.
• Practical scenarios – explore interactive governance scenarios using AI conversation tools in the Learning Management System.

Section B – Professional Practices

• Translating AI into policy – AI policy template, principles guiding policy, governance questions to resolve when drafting policy, the governance professional’s role in policy adoption.
• Preparing and maintaining governance artefacts – the quality of documentation to the board, preparing the AI board paper, maintaining the AI accountability register, minuting AI-related board discussion.
• Working with management – three disciplines that governance professionals should develop for professional practice.
• Practical scenarios – explore interactive governance scenarios using AI conversation tools in the Learning Management System.

Section C – Personal Thinking

• The parallel for AI governance with the Robodebt Scheme is direct. An organisation that approves an AI use case without establishing its legal authority, without named accountability, and without a mechanism for escalating concerns about its operation has reproduced the structural conditions of Robodebt at whatever scale its AI system operates.

  The automated income-matching process was the mechanism by which harm was delivered at scale, but the failures were governance failures - failures of legal authority, accountability, transparency, and ethical conduct. Those failures have direct relevance to how governance professionals must approach AI governance today.

  Case study: The Robodebt Scheme

Download course outline

A short course is assessed as follows:

• A final quiz (multiple choice, achieve 75% to pass, unlimited attempts).
• A short case study written response (suggested answers are revealed for reflection).
• Knowledge checks are included as you progress through the course material (these are not assessed).
• See the Assessment Rules for further information.

We provide specialised in-house training to organisations who want to train large groups of staff.

What are the benefits on in-house training?

• Train large numbers of staff.
• Participate in customised training that caters to the needs of your staff and your organisation’s business objectives.
• Invite and engage with your key clients, suppliers and contractors to ensure you’re working together with the same knowledge.

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